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Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation
To prove our claim, we design a novel human animation framework using a denoising diffusion model: a neural network learns to generate the image of a person by denoising temporal Gaussian noises whose intermediate results are cross-conditioned bidirectionally between consecutive frames.
Unrealistic Feature Suppression for Generative Adversarial Networks
However, sampling approaches which discard samples show limitations in some aspects such as the speed of training and optimality of the networks.
Spatially Decomposed Hinge Adversarial Loss by Local Gradient Amplifier
Generative Adversarial Networks (GANs) have achieved large attention and great success in various research areas, but it still suffers from training instability.
